Blending mindfulness practices and character strengths increases employee well‐being: A second‐order meta‐analysis and a follow‐up field experiment
Bibliographic record
Abstract
Abstract This study summarises the existing literature on Mindfulness‐Based Interventions (MBIs) and their effect on employee well‐being criteria and extends it by testing MBIs against a Mindfulness‐Strengths‐Based Intervention (MSBI). Given that extant MBIs focus on restoring well‐being, our first hypothesis was that MBIs would perform better on reducing negative emotional states than on promoting well‐being. To test our first hypothesis, we conducted a second‐order meta‐analysis, which summarised 13 first‐order meta‐analyses ( k = 311). MBIs had stronger effects on reducing negative emotions ( g = −0.74) than on increasing well‐being ( g = 0.58). Then, we conducted a follow‐up field experiment, comparing how an MSBI performed against an MBI on employee well‐being criteria. An MSBI combines mindful meditation, mindful living and Character‐Strengths‐Based Interventions. Our second hypothesis was that an MSBI would outperform an MBI on increasing employee well‐being criteria. During an MSBI, participants (a) attain a conscious state of mindful awareness, and (b) direct their attention towards the discovery and habitual exercise of their character strengths. To test our second hypothesis, we randomly assigned employees of a small Spanish healthcare organisation to either an MSBI or an MBI intervention group. We measured employee well‐being, before and after the intervention, using two well‐established measures of hedonic and eudaimonic well‐being. Our results show that both interventions were successful and had a large effect on both well‐being criteria. Further, as predicted, the MSBI group reported higher absolute scores of well‐being than the MBI group. Implications for theory and practice are discussed, and detailed appendices for practitioners are provided.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.024 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".